Artificial Intelligence Post Number 13

I have mentioned that semi-automatic or automatic driving cars will be one of the first applications of low-level artificial intelligence. The interest in this area just increased with recent highway death and injury data. Per the National Safety Council, the costs of deaths, injuries, and property damage are up 24% versus a year ago, to $152 billion for the first 6 months. To put this in perspective, the projected 2015 US deficit is $486 billion. If the year continues like the first six months, automobile accidents will cost the equivalent of 2/3 of our annual US deficit. This trend in increased highway deaths is unlikely to reverse given that “experts” believe that the causes are cheaper gas (which encourages people to drive more) along with people using phones and texting while driving, which we do not seem able (or willing) to stop. Note that this upward trend is happening while cars are getting measurably safer! The main problem is the driver, not the car!

Most car companies are developing various levels of computer and sensor driver aids, and even non-automotive companies like Google and Apple are investing heavily. But the company that is perhaps leading in this area is the Israel company Mobileye, which expects to have completely automated car technology within three years. Per their website, their EyeQ chip and algorithms are already in approximately 5 million vehicles. Although this EyeQ chip is not as unique as the IBM TrueNorth chip, it does enable what Mobileye calls parallel routing, up to four paths, between 4 masters and 4 slave ports. Autonomous driving is planned for launch in 2016. This will enable hands-free capable driving at highway speeds and in congested traffic. By 2018 they plan to add country roads and city traffic capability. They say this addition will be enabled by major algorithm changes they are already working on. Note that they believe that the hardware, with relatively minor and identified changes, is already capable.

The reason we are interested in this for AI is that that to truly have an automated car, the level of decision making by whatever computer is involved is extensive. Maybe not truly “thinking,” but it will appear that way if the car can really handle all the various scenarios it will have to deal with. To make the computer decision making as quick and energy efficient as possible, certainly novel computers like those built with the IBM TrueNorth chip will be considered. And the algorithms developed for automatic cars will certainly speed up applications in other areas that don’t have the potential $300 billion annual benefit of self-driving cars.

This work will not go unnoticed by those working towards truly thinking AI, because much of the improvements in computer architecture and advanced algorithms will be able to be applied in other areas, including projects whose goals are to emulate the way the human brain thinks, or its non-biological equivalence.